State-of-the-art deep-learning-based methods in writer identification and retrieval use a CNN to extract local image descriptors which are subsequently encoded using an encoding method. An encoding consists of two steps: 1) An embedding step, where local feature vectors are projected into a possibly high-dimensional space, and 2) An aggregation step, in which embedded local feature vectors of each input image are pooled into one global image descriptor. Christlein et al [1]} computed local feature vectors by means of ResNet-20 residual neural network and used the VLAD [2]} encoding method for embedding and aggregation. Building on the success of Christlein et al [1]} We propose a unified Neural network consisting of ResNet-20 followed by the trainable NetVLAD layer [4]}, inspired by the VLAD, at the head of the last convolutional layer of ResNet-20 to learn embedding of feature vectors in an end-to-end manner using a triplet loss [5]}.
